Using TensorFlow backend.
2019-07-19 15:25:41,899 - BBC.3.1.py:111- INFO - Start Training
WARNING:tensorflow:From /home/xiaorong/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
2019-07-19 15:25:44.578079: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2019-07-19 15:25:44.587875: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2199995000 Hz
2019-07-19 15:25:44.590222: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x564a44b38980 executing computations on platform Host. Devices:
2019-07-19 15:25:44.590244: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>
2019-07-19 15:25:44.973916: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x564a44bb9250 executing computations on platform CUDA. Devices:
2019-07-19 15:25:44.973969: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): GeForce RTX 2080 Ti, Compute Capability 7.5
2019-07-19 15:25:44.974591: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: 
name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.545
pciBusID: 0000:89:00.0
totalMemory: 10.73GiB freeMemory: 10.53GiB
2019-07-19 15:25:44.974616: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2019-07-19 15:25:44.977098: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-07-19 15:25:44.977117: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 
2019-07-19 15:25:44.977125: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N 
2019-07-19 15:25:44.977470: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10248 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:89:00.0, compute capability: 7.5)
WARNING:tensorflow:From /home/xiaorong/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3144: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 1, None, 128)      0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 128, None, 128)    1280      
_________________________________________________________________
batch_normalization_1 (Batch (None, 128, None, 128)    512       
_________________________________________________________________
activation_1 (Activation)    (None, 128, None, 128)    0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 128, None, 25)     0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 128, None, 25)     0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 128, None, 25)     147584    
_________________________________________________________________
batch_normalization_2 (Batch (None, 128, None, 25)     512       
_________________________________________________________________
activation_2 (Activation)    (None, 128, None, 25)     0         
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 128, None, 12)     0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 128, None, 12)     0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 128, None, 12)     147584    
_________________________________________________________________
batch_normalization_3 (Batch (None, 128, None, 12)     512       
_________________________________________________________________
activation_3 (Activation)    (None, 128, None, 12)     0         
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 128, None, 6)      0         
_________________________________________________________________
dropout_3 (Dropout)          (None, 128, None, 6)      0         
_________________________________________________________________
permute_1 (Permute)          (None, None, 128, 6)      0         
_________________________________________________________________
reshape_1 (Reshape)          (None, None, 768)         0         
_________________________________________________________________
bidirectional_1 (Bidirection (None, None, 32)          153792    
_________________________________________________________________
bidirectional_2 (Bidirection (None, None, 32)          12480     
_________________________________________________________________
time_distributed_1 (TimeDist (None, None, 32)          1056      
_________________________________________________________________
dropout_4 (Dropout)          (None, None, 32)          0         
_________________________________________________________________
time_distributed_2 (TimeDist (None, None, 6)           198       
_________________________________________________________________
strong_out (Activation)      (None, None, 6)           0         
=================================================================
Total params: 465,510
Trainable params: 464,742
Non-trainable params: 768
_________________________________________________________________
2019-07-19 15:25:47,792 - BBC.3.1.py:133- INFO - Train Parameters: frames_1_sec: 43, chunk_len: 5
2019-07-19 15:25:47,792 - BBC.3.1.py:135- INFO - Feature Parameters: chunk_size: 256, batch_size: 64, num_epochs: 100
2019-07-19 15:25:47,792 - BBC.3.1.py:137- INFO - Model Parameters: cnn_nb_filt: 128, cnn_pool_size: [5, 2, 2], rnn_nb: [32, 32], fc_nb: [32], dropout_rate: 0.5

WARNING:tensorflow:From /home/xiaorong/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
Epoch 1/100
2019-07-19 15:25:56.296916: I tensorflow/stream_executor/dso_loader.cc:152] successfully opened CUDA library libcublas.so.10.0 locally
 - 97s - loss: 0.5421 - val_loss: 0.4516
2019-07-19 15:27:46,228 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.0, ER_overall: 1.0, Best ER: inf, best_epoch: 0
Epoch 2/100
 - 83s - loss: 0.4145 - val_loss: 0.3094
2019-07-19 15:29:23,095 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.4124, ER_overall: 0.7227, Best ER: inf, best_epoch: 0
Epoch 3/100
 - 86s - loss: 0.3305 - val_loss: 0.2547
2019-07-19 15:31:02,085 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.6636, ER_overall: 0.4897, Best ER: inf, best_epoch: 0
Epoch 4/100
 - 85s - loss: 0.2687 - val_loss: 0.2516
2019-07-19 15:32:41,347 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.6469, ER_overall: 0.5018, Best ER: inf, best_epoch: 0
Epoch 5/100
 - 84s - loss: 0.2229 - val_loss: 0.2465
2019-07-19 15:34:19,250 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7348, ER_overall: 0.3705, Best ER: inf, best_epoch: 0
Epoch 6/100
 - 85s - loss: 0.1856 - val_loss: 0.1774
2019-07-19 15:35:57,449 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8285, ER_overall: 0.2703, Best ER: inf, best_epoch: 0
Epoch 7/100
 - 86s - loss: 0.1594 - val_loss: 0.2175
2019-07-19 15:37:36,769 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7781, ER_overall: 0.3056, Best ER: inf, best_epoch: 0
Epoch 8/100
 - 80s - loss: 0.1402 - val_loss: 0.1171
2019-07-19 15:39:09,541 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8898, ER_overall: 0.1525, Best ER: inf, best_epoch: 0
Epoch 9/100
 - 79s - loss: 0.1304 - val_loss: 0.2753
2019-07-19 15:40:40,609 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7061, ER_overall: 0.3771, Best ER: inf, best_epoch: 0
Epoch 10/100
 - 79s - loss: 0.1207 - val_loss: 0.1940
2019-07-19 15:42:12,702 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7928, ER_overall: 0.259, Best ER: inf, best_epoch: 0
Epoch 11/100
 - 82s - loss: 0.1064 - val_loss: 0.2535
2019-07-19 15:43:47,880 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7221, ER_overall: 0.3381, Best ER: inf, best_epoch: 0
Epoch 12/100
 - 81s - loss: 0.0969 - val_loss: 0.2422
2019-07-19 15:45:22,204 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7424, ER_overall: 0.3164, Best ER: inf, best_epoch: 0
Epoch 13/100
 - 81s - loss: 0.0914 - val_loss: 0.2807
2019-07-19 15:46:57,163 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7023, ER_overall: 0.3538, Best ER: inf, best_epoch: 0
Epoch 14/100
 - 84s - loss: 0.0841 - val_loss: 0.2440
2019-07-19 15:48:34,162 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7521, ER_overall: 0.2968, Best ER: inf, best_epoch: 0
Epoch 15/100
 - 83s - loss: 0.0768 - val_loss: 0.2708
2019-07-19 15:50:10,680 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7955, ER_overall: 0.2367, Best ER: inf, best_epoch: 0
Epoch 16/100
 - 85s - loss: 0.0733 - val_loss: 0.1950
2019-07-19 15:51:49,223 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8321, ER_overall: 0.1999, Best ER: inf, best_epoch: 0
Epoch 17/100
 - 83s - loss: 0.0694 - val_loss: 0.3446
2019-07-19 15:53:25,599 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.6827, ER_overall: 0.3668, Best ER: inf, best_epoch: 0
Epoch 18/100
 - 84s - loss: 0.0718 - val_loss: 0.2689
2019-07-19 15:55:02,794 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7473, ER_overall: 0.2961, Best ER: inf, best_epoch: 0
Epoch 19/100
 - 85s - loss: 0.0656 - val_loss: 0.2057
2019-07-19 15:56:41,994 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8262, ER_overall: 0.2067, Best ER: inf, best_epoch: 0
Epoch 20/100
 - 82s - loss: 0.0651 - val_loss: 0.2206
2019-07-19 15:58:16,411 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7969, ER_overall: 0.2537, Best ER: inf, best_epoch: 0
Epoch 21/100
 - 84s - loss: 0.0605 - val_loss: 0.1946
2019-07-19 15:59:54,399 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8468, ER_overall: 0.185, Best ER: inf, best_epoch: 0
Epoch 22/100
 - 83s - loss: 0.0607 - val_loss: 0.2390
2019-07-19 16:01:31,161 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8196, ER_overall: 0.2167, Best ER: inf, best_epoch: 0
Epoch 23/100
 - 85s - loss: 0.0565 - val_loss: 0.2627
2019-07-19 16:03:09,164 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8081, ER_overall: 0.2257, Best ER: inf, best_epoch: 0
Epoch 24/100
 - 84s - loss: 0.0523 - val_loss: 0.3767
2019-07-19 16:04:47,443 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7115, ER_overall: 0.3432, Best ER: inf, best_epoch: 0
Epoch 25/100
 - 83s - loss: 0.0561 - val_loss: 0.2558
2019-07-19 16:06:24,337 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8157, ER_overall: 0.2178, Best ER: inf, best_epoch: 0
Epoch 26/100
 - 84s - loss: 0.0555 - val_loss: 0.2153
2019-07-19 16:08:02,015 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8385, ER_overall: 0.2007, Best ER: inf, best_epoch: 0
Epoch 27/100
 - 83s - loss: 0.0518 - val_loss: 0.3105
2019-07-19 16:09:38,112 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7284, ER_overall: 0.328, Best ER: inf, best_epoch: 0
Epoch 28/100
 - 84s - loss: 0.0490 - val_loss: 0.2740
2019-07-19 16:11:15,499 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7955, ER_overall: 0.2464, Best ER: inf, best_epoch: 0
Epoch 29/100
 - 81s - loss: 0.0481 - val_loss: 0.3523
2019-07-19 16:12:50,590 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7593, ER_overall: 0.2822, Best ER: inf, best_epoch: 0
Epoch 30/100
 - 83s - loss: 0.0504 - val_loss: 0.1627
2019-07-19 16:14:26,672 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8845, ER_overall: 0.1432, Best ER: inf, best_epoch: 0
Epoch 31/100
 - 82s - loss: 0.0449 - val_loss: 0.1831
2019-07-19 16:16:02,346 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8615, ER_overall: 0.1657, Best ER: inf, best_epoch: 0
Epoch 32/100
 - 82s - loss: 0.0443 - val_loss: 0.3814
2019-07-19 16:17:37,171 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7308, ER_overall: 0.3127, Best ER: inf, best_epoch: 0
Epoch 33/100
 - 82s - loss: 0.0430 - val_loss: 0.3380
2019-07-19 16:19:12,356 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7793, ER_overall: 0.2534, Best ER: inf, best_epoch: 0
Epoch 34/100
 - 80s - loss: 0.0394 - val_loss: 0.2383
2019-07-19 16:20:46,196 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8316, ER_overall: 0.1946, Best ER: inf, best_epoch: 0
Epoch 35/100
 - 82s - loss: 0.0393 - val_loss: 0.2163
2019-07-19 16:22:20,427 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8474, ER_overall: 0.1777, Best ER: inf, best_epoch: 0
Epoch 36/100
 - 82s - loss: 0.0374 - val_loss: 0.3285
2019-07-19 16:23:55,173 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8107, ER_overall: 0.2145, Best ER: inf, best_epoch: 0
Epoch 37/100
 - 82s - loss: 0.0379 - val_loss: 0.2227
2019-07-19 16:25:29,787 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8592, ER_overall: 0.1687, Best ER: inf, best_epoch: 0
Epoch 38/100
 - 82s - loss: 0.0340 - val_loss: 0.2871
2019-07-19 16:27:04,915 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8174, ER_overall: 0.2095, Best ER: inf, best_epoch: 0
Epoch 39/100
 - 81s - loss: 0.0373 - val_loss: 0.2357
2019-07-19 16:28:39,682 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8358, ER_overall: 0.1888, Best ER: inf, best_epoch: 0
Epoch 40/100
 - 82s - loss: 0.0379 - val_loss: 0.1650
2019-07-19 16:30:14,493 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8907, ER_overall: 0.1304, Best ER: inf, best_epoch: 0
Epoch 41/100
 - 83s - loss: 0.0351 - val_loss: 0.3395
2019-07-19 16:31:50,846 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7785, ER_overall: 0.2602, Best ER: inf, best_epoch: 0
Epoch 42/100
 - 81s - loss: 0.0334 - val_loss: 0.1980
2019-07-19 16:33:24,487 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8297, ER_overall: 0.2122, Best ER: inf, best_epoch: 0
Epoch 43/100
 - 82s - loss: 0.0334 - val_loss: 0.2073
2019-07-19 16:34:59,460 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8598, ER_overall: 0.1667, Best ER: inf, best_epoch: 0
Epoch 44/100
 - 83s - loss: 0.0350 - val_loss: 0.2269
2019-07-19 16:36:35,452 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.852, ER_overall: 0.1731, Best ER: inf, best_epoch: 0
Epoch 45/100
 - 81s - loss: 0.0344 - val_loss: 0.1796
2019-07-19 16:38:10,054 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8632, ER_overall: 0.167, Best ER: inf, best_epoch: 0
Epoch 46/100
 - 81s - loss: 0.0328 - val_loss: 0.1900
2019-07-19 16:39:43,731 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8511, ER_overall: 0.1733, Best ER: inf, best_epoch: 0
Epoch 47/100
 - 82s - loss: 0.0287 - val_loss: 0.3866
2019-07-19 16:41:19,652 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7355, ER_overall: 0.3065, Best ER: inf, best_epoch: 0
Epoch 48/100
 - 81s - loss: 0.0302 - val_loss: 0.2066
2019-07-19 16:42:53,577 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8414, ER_overall: 0.1868, Best ER: inf, best_epoch: 0
Epoch 49/100
 - 80s - loss: 0.0315 - val_loss: 0.2370
2019-07-19 16:44:26,851 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8573, ER_overall: 0.1705, Best ER: inf, best_epoch: 0
Epoch 50/100
 - 81s - loss: 0.0288 - val_loss: 0.3103
2019-07-19 16:46:01,287 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8276, ER_overall: 0.2081, Best ER: inf, best_epoch: 0
Epoch 51/100
 - 80s - loss: 0.0281 - val_loss: 0.3042
2019-07-19 16:47:34,665 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7981, ER_overall: 0.2405, Best ER: inf, best_epoch: 0
Epoch 52/100
 - 81s - loss: 0.0314 - val_loss: 0.2645
2019-07-19 16:49:09,260 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8383, ER_overall: 0.1868, Best ER: inf, best_epoch: 0
Epoch 53/100
 - 81s - loss: 0.0289 - val_loss: 0.2457
2019-07-19 16:50:43,563 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8342, ER_overall: 0.1985, Best ER: inf, best_epoch: 0
Epoch 54/100
 - 80s - loss: 0.0275 - val_loss: 0.3767
2019-07-19 16:52:16,986 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7655, ER_overall: 0.2764, Best ER: inf, best_epoch: 0
Epoch 55/100
 - 81s - loss: 0.0284 - val_loss: 0.2763
2019-07-19 16:53:50,595 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8348, ER_overall: 0.1969, Best ER: inf, best_epoch: 0
Epoch 56/100
 - 81s - loss: 0.0256 - val_loss: 0.3499
2019-07-19 16:55:24,574 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7836, ER_overall: 0.2588, Best ER: inf, best_epoch: 0
Epoch 57/100
 - 80s - loss: 0.0282 - val_loss: 0.2632
2019-07-19 16:56:57,528 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8284, ER_overall: 0.2056, Best ER: inf, best_epoch: 0
Epoch 58/100
 - 80s - loss: 0.0272 - val_loss: 0.2587
2019-07-19 16:58:29,615 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8115, ER_overall: 0.226, Best ER: inf, best_epoch: 0
Epoch 59/100
 - 81s - loss: 0.0234 - val_loss: 0.2949
2019-07-19 17:00:04,089 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8299, ER_overall: 0.2023, Best ER: inf, best_epoch: 0
Epoch 60/100
 - 81s - loss: 0.0246 - val_loss: 0.2004
2019-07-19 17:01:39,015 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8803, ER_overall: 0.1413, Best ER: inf, best_epoch: 0
Epoch 61/100
 - 81s - loss: 0.0242 - val_loss: 0.1936
2019-07-19 17:03:13,477 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8751, ER_overall: 0.1504, Best ER: inf, best_epoch: 0
Epoch 62/100
 - 81s - loss: 0.0254 - val_loss: 0.3557
2019-07-19 17:04:47,633 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7851, ER_overall: 0.2537, Best ER: inf, best_epoch: 0
Epoch 63/100
 - 80s - loss: 0.0253 - val_loss: 0.3200
2019-07-19 17:06:20,661 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8231, ER_overall: 0.2057, Best ER: inf, best_epoch: 0
Epoch 64/100
 - 80s - loss: 0.0250 - val_loss: 0.3340
2019-07-19 17:07:54,331 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7913, ER_overall: 0.2484, Best ER: inf, best_epoch: 0
Epoch 65/100
 - 83s - loss: 0.0253 - val_loss: 0.1727
2019-07-19 17:09:29,983 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8852, ER_overall: 0.1391, Best ER: inf, best_epoch: 0
Epoch 66/100
 - 81s - loss: 0.0242 - val_loss: 0.1759
2019-07-19 17:11:04,624 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8951, ER_overall: 0.1263, Best ER: inf, best_epoch: 0
Epoch 67/100
 - 81s - loss: 0.0233 - val_loss: 0.2002
2019-07-19 17:12:39,661 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8832, ER_overall: 0.1382, Best ER: inf, best_epoch: 0
Epoch 68/100
 - 81s - loss: 0.0237 - val_loss: 0.2942
2019-07-19 17:14:13,155 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8345, ER_overall: 0.1972, Best ER: inf, best_epoch: 0
Epoch 69/100
 - 81s - loss: 0.0249 - val_loss: 0.2526
2019-07-19 17:15:47,762 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8474, ER_overall: 0.189, Best ER: inf, best_epoch: 0
Epoch 70/100
 - 80s - loss: 0.0239 - val_loss: 0.2510
2019-07-19 17:17:20,645 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8479, ER_overall: 0.1805, Best ER: inf, best_epoch: 0
Epoch 71/100
 - 81s - loss: 0.0235 - val_loss: 0.2099
2019-07-19 17:18:54,898 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.885, ER_overall: 0.139, Best ER: inf, best_epoch: 0
Epoch 72/100
 - 80s - loss: 0.0202 - val_loss: 0.3438
2019-07-19 17:20:27,896 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8216, ER_overall: 0.2092, Best ER: inf, best_epoch: 0
Epoch 73/100
 - 82s - loss: 0.0234 - val_loss: 0.3678
2019-07-19 17:22:03,079 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7965, ER_overall: 0.2377, Best ER: inf, best_epoch: 0
Epoch 74/100
 - 81s - loss: 0.0242 - val_loss: 0.2766
2019-07-19 17:23:37,747 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.846, ER_overall: 0.1852, Best ER: inf, best_epoch: 0
Epoch 75/100
 - 80s - loss: 0.0231 - val_loss: 0.3099
2019-07-19 17:25:11,414 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7915, ER_overall: 0.2464, Best ER: inf, best_epoch: 0
Epoch 76/100
 - 83s - loss: 0.0236 - val_loss: 0.2520
2019-07-19 17:26:48,177 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.853, ER_overall: 0.1742, Best ER: inf, best_epoch: 0
Epoch 77/100
 - 82s - loss: 0.0228 - val_loss: 0.2739
2019-07-19 17:28:24,186 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8444, ER_overall: 0.1915, Best ER: inf, best_epoch: 0
Epoch 78/100
 - 80s - loss: 0.0233 - val_loss: 0.2674
2019-07-19 17:29:57,342 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.856, ER_overall: 0.1749, Best ER: inf, best_epoch: 0
Epoch 79/100
 - 79s - loss: 0.0213 - val_loss: 0.2009
2019-07-19 17:31:29,798 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.876, ER_overall: 0.1501, Best ER: inf, best_epoch: 0
Epoch 80/100
 - 80s - loss: 0.0185 - val_loss: 0.1602
2019-07-19 17:33:03,342 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8981, ER_overall: 0.13, Best ER: inf, best_epoch: 0
Epoch 81/100
 - 83s - loss: 0.0183 - val_loss: 0.4162
2019-07-19 17:34:39,973 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7793, ER_overall: 0.2703, Best ER: 0.2703, best_epoch: 80
Epoch 82/100
 - 84s - loss: 0.0206 - val_loss: 0.2128
2019-07-19 17:36:18,050 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8747, ER_overall: 0.152, Best ER: 0.152, best_epoch: 81
Epoch 83/100
 - 87s - loss: 0.0207 - val_loss: 0.3204
2019-07-19 17:37:58,733 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8267, ER_overall: 0.2072, Best ER: 0.152, best_epoch: 81
Epoch 84/100
 - 84s - loss: 0.0175 - val_loss: 0.1439
2019-07-19 17:39:36,341 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.9111, ER_overall: 0.108, Best ER: 0.108, best_epoch: 83
Epoch 85/100
 - 83s - loss: 0.0211 - val_loss: 0.3864
2019-07-19 17:41:12,350 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7904, ER_overall: 0.2519, Best ER: 0.108, best_epoch: 83
Epoch 86/100
 - 81s - loss: 0.0191 - val_loss: 0.3287
2019-07-19 17:42:47,294 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8094, ER_overall: 0.2261, Best ER: 0.108, best_epoch: 83
Epoch 87/100
 - 82s - loss: 0.0206 - val_loss: 0.3972
2019-07-19 17:44:22,914 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7871, ER_overall: 0.254, Best ER: 0.108, best_epoch: 83
Epoch 88/100
 - 81s - loss: 0.0181 - val_loss: 0.3210
2019-07-19 17:45:57,545 - model_base.py:94- INFO - LR: 0.0008, F1_overall: 0.8231, ER_overall: 0.2035, Best ER: 0.108, best_epoch: 83
Epoch 89/100
 - 85s - loss: 0.0175 - val_loss: 0.3921
2019-07-19 17:47:35,695 - model_base.py:94- INFO - LR: 0.0008, F1_overall: 0.8282, ER_overall: 0.1966, Best ER: 0.108, best_epoch: 83
Epoch 90/100
 - 84s - loss: 0.0163 - val_loss: 0.2895
2019-07-19 17:49:12,856 - model_base.py:94- INFO - LR: 0.00064, F1_overall: 0.8589, ER_overall: 0.1687, Best ER: 0.108, best_epoch: 83
Epoch 91/100
 - 84s - loss: 0.0155 - val_loss: 0.1946
2019-07-19 17:50:50,927 - model_base.py:94- INFO - LR: 0.00064, F1_overall: 0.898, ER_overall: 0.1284, Best ER: 0.108, best_epoch: 83
Epoch 92/100
 - 81s - loss: 0.0147 - val_loss: 0.2078
2019-07-19 17:52:25,381 - model_base.py:94- INFO - LR: 0.000512, F1_overall: 0.8931, ER_overall: 0.1356, Best ER: 0.108, best_epoch: 83
Epoch 93/100
 - 81s - loss: 0.0151 - val_loss: 0.2462
2019-07-19 17:53:59,699 - model_base.py:94- INFO - LR: 0.000512, F1_overall: 0.8681, ER_overall: 0.1605, Best ER: 0.108, best_epoch: 83
Epoch 94/100
 - 84s - loss: 0.0138 - val_loss: 0.2104
2019-07-19 17:55:38,151 - model_base.py:94- INFO - LR: 0.0004096, F1_overall: 0.8817, ER_overall: 0.145, Best ER: 0.108, best_epoch: 83
Epoch 95/100
 - 83s - loss: 0.0132 - val_loss: 0.2812
2019-07-19 17:57:13,948 - model_base.py:94- INFO - LR: 0.0004096, F1_overall: 0.8565, ER_overall: 0.1721, Best ER: 0.108, best_epoch: 83
Epoch 96/100
 - 83s - loss: 0.0147 - val_loss: 0.2715
2019-07-19 17:58:51,395 - model_base.py:94- INFO - LR: 0.00032768, F1_overall: 0.8599, ER_overall: 0.1687, Best ER: 0.108, best_epoch: 83
Epoch 97/100
 - 82s - loss: 0.0130 - val_loss: 0.2913
2019-07-19 18:00:27,933 - model_base.py:94- INFO - LR: 0.00032768, F1_overall: 0.8564, ER_overall: 0.1698, Best ER: 0.108, best_epoch: 83
Epoch 98/100
 - 81s - loss: 0.0126 - val_loss: 0.2845
2019-07-19 18:02:03,041 - model_base.py:94- INFO - LR: 0.000262144, F1_overall: 0.8586, ER_overall: 0.1687, Best ER: 0.108, best_epoch: 83
Epoch 99/100
 - 81s - loss: 0.0113 - val_loss: 0.2612
2019-07-19 18:03:37,616 - model_base.py:94- INFO - LR: 0.000262144, F1_overall: 0.87, ER_overall: 0.1551, Best ER: 0.108, best_epoch: 83
Epoch 100/100
 - 81s - loss: 0.0115 - val_loss: 0.2459
2019-07-19 18:05:12,259 - model_base.py:94- INFO - LR: 0.000209715, F1_overall: 0.8719, ER_overall: 0.1538, Best ER: 0.108, best_epoch: 83
2019-07-19 18:05:12,459 - BBC.3.1.py:164- INFO - Start decoding
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/home/xiaorong/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
/home/xiaorong/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1145: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
[[  53    0    3    0    0    0    5]
 [   0    8    1    0    2    0    7]
 [  20   35 1461   10   10    0  132]
 [   0    0   15  456    0    0    4]
 [   0    0    7    0    0    0    2]
 [   0    2    0    4    0    0   18]
 [   0    0    0    0    0    0    0]]
              precision    recall  f1-score   support

       Siren       0.73      0.87      0.79        61
     Thunder       0.18      0.44      0.25        18
         Car       0.98      0.88      0.93      1668
        Foot       0.97      0.96      0.97       475
        Fire       0.00      0.00      0.00         9
        Door       0.00      0.00      0.00        24
     unknown       0.00      0.00      0.00         0

   micro avg       0.88      0.88      0.88      2255
   macro avg       0.41      0.45      0.42      2255
weighted avg       0.95      0.88      0.91      2255

2019-07-19 20:00:48,678 - BBC.3.1.py:215- INFO - Test utt-level acc: 0.877162
2019-07-19 20:00:48,678 - BBC.3.1.py:216- INFO - End decoding
